Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series | Paper Citation Record

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This paper cites Nonintrusive appliance load monitoring,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Nonintrusive appliance load monitoring,

Reference 1

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Observation 853c5e14-ab3b-4f4d-ac43-16969b704256 · outbound

This paper cites Redd: A public data set for energy disaggregation research,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Redd: A public data set for energy disaggregation research,

Reference 2

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This paper cites The uk-dale dataset, domestic appliance- level electricity demand and whole-house demand from five uk homes,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series The uk-dale dataset, domestic appliance- level electricity demand and whole-house demand from five uk homes,

Reference 3

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This paper cites REFIT Smart Home dataset,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series REFIT Smart Home dataset,

Reference 4

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This paper cites Multilabel appliance classifica- tion with weakly labeled data for non-intrusive load monitoring,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Multilabel appliance classifica- tion with weakly labeled data for non-intrusive load monitoring,

Reference 5

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This paper cites Residential appliance detection using attention-based deep convolutional neural network,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Residential appliance detection using attention-based deep convolutional neural network,

Reference 6

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This paper cites Appliance detection using very low-frequency smart meter time series,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Appliance detection using very low-frequency smart meter time series,

Reference 7

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This paper cites Adf & transapp: A transformer-based framework for appliance detection using smart meter consumption series,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Adf & transapp: A transformer-based framework for appliance detection using smart meter consumption series,

Reference 8

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Observation 9da7e50a-aa3f-4338-be6d-3f788520b566 · outbound

This paper cites Limesegment: Meaningful, realistic time series explanations,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Limesegment: Meaningful, realistic time series explanations,

Reference 9

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Observation ad8765f5-488b-4554-8110-7f9bbb93e486 · outbound

This paper cites A unified approach to interpreting model predictions,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series A unified approach to interpreting model predictions,

Reference 10

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Observation 420023ed-904e-4a08-beec-857e65e8de0c · outbound

This paper cites Learning deep features for discriminative localization,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Learning deep features for discriminative localization,

Reference 11

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Observation 69a3cf4a-7f70-4822-ba7b-08bb702cea80 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 12

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Observation ba5c5870-a3ad-4750-9da4-04a5e9c95269 · outbound

This paper cites Dcam: Dimension- wise class activation map for explaining multivariate data series classification,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Dcam: Dimension- wise class activation map for explaining multivariate data series classification,

Reference 13

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This paper cites Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

Reference 14

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This paper cites dcnn/dcam: anomaly precursors discovery in multivariate time series with deep convolutional neural networks,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series dcnn/dcam: anomaly precursors discovery in multivariate time series with deep convolutional neural networks,

Reference 15

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Observation cf87f70d-e3f7-4233-aafa-a2eebdc2e8d1 · outbound

This paper cites Snorkel: rapid training data creation with weak supervision,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Snorkel: rapid training data creation with weak supervision,

Reference 16

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This paper cites Snorkel: Fast training set generation for information extraction,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Snorkel: Fast training set generation for information extraction,

Reference 17

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Observation 68e81f9b-2fa4-4138-ae57-5168589bab38 · outbound

This paper cites A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (nilm) in residential context,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (nilm) in residential context,

Reference 18

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Observation 63bcebd1-2ab7-4ebb-9382-c0e6a362e85f · outbound

This paper cites Electricity demand activation extraction: From known to unknown signatures, using similarity search,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Electricity demand activation extraction: From known to unknown signatures, using similarity search,

Reference 19

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This paper cites Towards trustworthy energy disaggregation: A review of challenges, methods, and perspectives for non-intrusive load moni- toring,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Towards trustworthy energy disaggregation: A review of challenges, methods, and perspectives for non-intrusive load moni- toring,

Reference 20

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This paper cites Unsupervised disaggregation of low frequency power measurements,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Unsupervised disaggregation of low frequency power measurements,

Reference 21

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This paper cites Neural NILM,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Neural NILM,

Reference 22

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This paper cites Sequence- to-point learning with neural networks for non-intrusive load mon- itoring,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Sequence- to-point learning with neural networks for non-intrusive load mon- itoring,

Reference 23

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This paper cites Dilated residual attention network for load disaggregation,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Dilated residual attention network for load disaggregation,

Reference 24

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This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series U-net: Convolutional networks for biomedical image segmentation,

Reference 25

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This paper cites Non-intrusive load disaggregation by convolutional neural network and multilabel classification,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Non-intrusive load disaggregation by convolutional neural network and multilabel classification,

Reference 26

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This paper cites Thresholding methods in non-intrusive load monitoring,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Thresholding methods in non-intrusive load monitoring,

Reference 28

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 29

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This paper cites Bert4nilm: A bidirectional transformer model for non-intrusive load monitoring,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Bert4nilm: A bidirectional transformer model for non-intrusive load monitoring,

Reference 30

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This paper cites Transnilm: A transformer-based deep learning model for non-intrusive load moni- toring,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Transnilm: A transformer-based deep learning model for non-intrusive load moni- toring,

Reference 31

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This paper cites Timeshap: Explaining recurrent models through sequence perturbations,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Timeshap: Explaining recurrent models through sequence perturbations,

Reference 32

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This paper cites Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks,

Reference 33

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This paper cites Score-cam: Score-weighted visual explanations for convo- lutional neural networks,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Score-cam: Score-weighted visual explanations for convo- lutional neural networks,

Reference 34

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This paper cites Rocket: exceptionally fast and accurate time series classification using random convolutional kernels,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Rocket: exceptionally fast and accurate time series classification using random convolutional kernels,

Reference 35

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This paper cites MiniRocket: A very fast (almost) deterministic transform for time series classification,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series MiniRocket: A very fast (almost) deterministic transform for time series classification,

Reference 36

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This paper cites InceptionTime: Finding AlexNet for time series classification,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series InceptionTime: Finding AlexNet for time series classification,

Reference 37

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This paper cites Deep residual learning for image recognition,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Deep residual learning for image recognition,

Reference 38

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This paper cites Deep learning for time series classification: A review,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Deep learning for time series classification: A review,

Reference 39

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This paper cites Petralia.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Petralia

Reference 40

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Observation a878a517-0314-4f25-a06c-f44a7c553d3e · outbound

This paper cites Devicescope: An interactive app to detect and localize appliance patterns in electricity consumption time series,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Devicescope: An interactive app to detect and localize appliance patterns in electricity consumption time series,

Reference 41

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This paper cites The ideal household energy dataset, electricity, gas, contextual sensor data and survey data for 255 uk homes,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series The ideal household energy dataset, electricity, gas, contextual sensor data and survey data for 255 uk homes,

Reference 42

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This paper cites Load disaggregation technologies: Real world and laboratory performance,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Load disaggregation technologies: Real world and laboratory performance,

Reference 43

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This paper cites ´Evolution du taux moyen d’ ´emissions de CO2 en France,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series ´Evolution du taux moyen d’ ´emissions de CO2 en France,

Reference 44

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This paper cites Se d ´eplacer au quotidien : enjeux spatiaux, enjeux sociaux,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Se d ´eplacer au quotidien : enjeux spatiaux, enjeux sociaux,

Reference 45

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This paper cites What’s the Carbon Footprint of Your Website?.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series What’s the Carbon Footprint of Your Website?

Reference 46

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This paper cites On the impact of the sequence length on sequence-to-sequence and sequence-to-point learning for nilm,.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series On the impact of the sequence length on sequence-to-sequence and sequence-to-point learning for nilm,

Reference 47

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This paper cites Available: https://api.semanticscholar.org/CorpusID: 6789015.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Available: https://api.semanticscholar.org/CorpusID: 6789015

Reference 2015

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This paper cites Available: https://api.semanticscholar.org/CorpusID: 52967399.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Available: https://api.semanticscholar.org/CorpusID: 52967399

Reference 2019

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This paper cites Available: https://doi.org/10.14778/3632093.3632115.

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Available: https://doi.org/10.14778/3632093.3632115

Reference 2023

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